2 papers
cs.LG2026
Learning Fair Domain Adaptation with Virtual Label Distribution
Yuguang Zhang, Lijun Sheng, Jian Liang +1
Unsupervised Domain Adaptation (UDA) aims to mitigate performance degradation when training and testing data are sampled from different distributions. While significant progress ha…
cs.LG2024
STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory Replay
Yongcan Yu, Lijun Sheng, Ran He +1
Test-time adaptation (TTA) aims to address the distribution shift between the training and test data with only unlabeled data at test time. Existing TTA methods often focus on impr…